Abstract
Abstract. Quantitative retrieval of land surface biological parameters (e.g. foliage projective cover [FPC] and Leaf Area Index) is crucial for forest management, ecosystem modelling, and global change monitoring applications. Currently, remote sensing is a widely adopted method for rapid estimation of surface biological parameters in a landscape scale. Topographic correction is a necessary pre-processing step in the remote sensing application for topographically complex terrain. Selection of a suitable topographic correction method on remotely sensed spectral information is still an unresolved problem. The purpose of this study is to assess the impact of topographic corrections on the prediction of FPC in hilly terrain using an established regression model. Five established topographic corrections [C, Minnaert, SCS, SCS+C and processing scheme for standardised surface reflectance (PSSSR)] were evaluated on Landsat TM5 acquired under low and high sun angles in closed canopied subtropical rainforest and eucalyptus dominated open canopied forest, north-eastern Australia. The effectiveness of methods at normalizing topographic influence, preserving biophysical spectral information, and internal data variability were assessed by statistical analysis and by comparing field collected FPC data. The results of statistical analyses show that SCS+C and PSSSR perform significantly better than other corrections, which were on less overcorrected areas of faintly illuminated slopes. However, the best relationship between FPC and Landsat spectral responses was obtained with the PSSSR by producing the least residual error. The SCS correction method was poor for correction of topographic effect in predicting FPC in topographically complex terrain.
Highlights
Operational mapping and monitoring of vegetation cover and vegetation cover changes is an important application of remote sensing
Large area monitoring of vegetation cover depends on the relationship between field measurements of vegetation cover and spectral reflectance measured by remote sensors
The principal polar spectral greenness (PPSG) index (Moffiet et al, 2010) exhibits that property and, in a later, paper will be subject to evaluation against the best terrain correction methods which we find here
Summary
Operational mapping and monitoring of vegetation cover and vegetation cover changes is an important application of remote sensing. Remote sensing of rugged terrain, mainly characterized by high topographic relief, presents unique challenges in monitoring biophysical attributes not encountered on flat terrain. This change affects both the amount of light illuminating the surface and the amount of light entering the sensor. Topographic correction is a necessary pre-processing step in the remote sensing application for topographically complex terrain. It is possible, for a spectral greenness algorithm to be developed which is independent of terrain induced brightness variation and will not need topographic correction.
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